Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties
Abstract
The extracellular matrix (ECM) plays an instrumental role in determining the spatial orientation of epithelial polarity and the formation of lumens in glandular tissues during morphogenesis. Here, we show that the Endoplasmic Reticulum (ER)-resident protein anterior gradient-2 (AGR2), a soluble protein-disulfide isomerase involved in ER protein folding and quality control, is secreted and interacts with the ECM. Extracellular AGR2 (eAGR2) is a microenvironmental regulator of epithelial tissue architecture, which plays a role in the preneoplastic phenotype and contributes to epithelial tumorigenicity. Indeed, eAGR2, is secreted as a functionally active protein independently of its thioredoxin-like domain (CXXS) and of its ER-retention domain (KTEL), and is sufficient, by itself, to promote the acquisition of invasive and metastatic features. Therefore, we conclude that eAGR2 plays an extracellular role independent of its ER function and we elucidate this gain-of-function as a novel and unexpected critical ECM microenvironmental pro-oncogenic regulator of epithelial morphogenesis and tumorigenesis.
Article and author information
Author details
Reviewing Editor
- Johanna Ivaska, University of Turku, Finland
Ethics
Animal experimentation: All animal procedures met the European Community Directive guidelines (Agreement B33-522-2/ Number DIR 1322) and were approved by the ethical committee from Bordeaux University.
Human subjects: Samples of human lung cancer tissues were obtained from the Haut-Leveque University Hospital (Bordeaux, France) and reviewed by expert pathologist in the field (H. Begueret). These procedures were approved by the Institutional Review Board at Haut-Leveque (NFS96900 Certification).
Version history
- Received: December 17, 2015
- Accepted: May 28, 2016
- Accepted Manuscript published: May 30, 2016 (version 1)
- Version of Record published: July 11, 2016 (version 2)
Copyright
© 2016, Fessart et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 4,071
- Page views
-
- 1,227
- Downloads
-
- 63
- Citations
Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium. In vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high NUPR1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.
-
- Cancer Biology
- Computational and Systems Biology
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.